Summary
“Range: Why Generalists Triumph in a Specialized World”
Why this book?
I have always held the belief that narrow specialization is the way toward achieving any remarkable success in most fields, including science and engineering. That knowing about an area more than anyone does is what sets us apart. I have looked askance at those who know something about many things but not so much about anything! This despise of generalists was a major motivation for me to pursue a graduate degree. I wanted to master ‘something’.
The book at hand has seriously challenged this belief and helped me adopt a more nuanced view. Here are a few highlights, followed by some of my favorite passages.
Highlights
Experience is overrated
Several social studies have shown that experts are more prone to making mistakes when they encounter a new situation.
A manifestation of this observation is the failure of experts to predict the future. The psychologist Philip Tetlock asked 284 highly educated experts to make specific predictions about specific future events and tracked their predictions for 20 years. The so called “experts” have done horribly. As Tetlock put it, they were not better than dart-throwing chimps.
Among high achievers, early specialization is not the rule
It’s a common belief that high achievers start learning and practicing early, which give them a head start. This contradicts with many observations.
German scientists published a study in 2014 showing that members of their national team, which had just won the World Cup, were typically late specializers who didn’t play more organized soccer than amateur-league players until age twenty-two or later.
The recommendation: experimentation
In order to contribute something meaningful, a more optimal path than early specialization is experimenting in many fields and then focusing on solving a particular problem, aided with the breadth gained while experimenting.
Excerpts
Everyone needs habits of mind that allow them to dance across disciplines… The goal is not just to transfer knowledge, but to raise fundamental questions and to become familiar with the powerful ideas that shape our society.
Jeannette Wing, a computer science professor at Columbia University and former corporate vice president of Microsoft Research, has pushed broad “computational thinking” as the mental Swiss Army knife. She advocated that it become as fundamental as reading, even for those who will have nothing to do with computer science or programming. “Computational thinking is using abstraction and decomposition when attacking a large complex task,” she wrote. “It is choosing an appropriate representation for a problem.
Like chess masters and firefighters, premodern villagers relied on things being the same tomorrow as they were yesterday. They were extremely well prepared for what they had experienced before, and extremely poorly equipped for everything else. Their very thinking was highly specialized in a manner that the modern world has been telling us is increasingly obsolete. They were perfectly capable of learning from experience, but failed at learning without experience. And that is what a rapidly changing, wicked world demands—conceptual reasoning skills that can connect new ideas and work across contexts. Faced with any problem they had not directly experienced before, the remote villagers were completely lost. That is not an option for us. The more constrained and repetitive a challenge, the more likely it will be automated, while great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one.
They were perfectly capable of learning from experience, but failed at learning without experience
For learning that is both durable (it sticks) and flexible (it can be applied broadly), fast and easy is precisely the problem. [A study showed that] being forced to generate answers improves subsequent learning even if the generated answer is wrong. It can even help to be wildly wrong.
Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful. The struggle is real, and really useful. “Like life,” Kornell and team wrote, “retrieval is all about the journey. Struggling to hold on to information and then recall it had helped the group distracted by math problems transfer the information from short-term to long-term memory. The group with more and immediate rehearsal opportunity recalled nearly nothing on the pop quiz. Repetition, it turned out, was less important than struggle.
How useful is a head start? Closed vs. open skills:
In 2017, Greg Duncan, the education economist, along with psychologist Drew Bailey and colleagues, reviewed sixty-seven early childhood education programs meant to boost academic achievement. Programs like Head Start did give a head start, but academically that was about it. The researchers found a pervasive “fadeout” effect, where a temporary academic advantage quickly diminished and often completely vanished. On a graph, it looks eerily like the kind that show future elite athletes catching up to their peers who got a head start in deliberate practice.
A reason for this, the researchers concluded, is that early childhood education programs teach “closed” skills that can be acquired quickly with repetition of procedures, but that everyone will pick up at some point anyway. The fadeout was not a disappearance of skill so much as the rest of the world catching up. The motor-skill equivalent would be teaching a kid to walk a little early. Everyone is going to learn it anyway, and while it might be temporarily impressive, there is no evidence that rushing it matters.
The research team recommended that if programs want to impart lasting academic benefits they should focus instead on “open” skills that scaffold later knowledge. Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they read can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. “The slowest growth,” the researchers wrote, occurs “for the most complex skills.”
“In a wicked world, relying upon experience from a single domain is not only limiting, it can be disastrous.”